Income-related inequality in quality-adjusted life expectancy in Korea at the national and district levels

Abstract

Background: The aim of this study was to measure differences in quality-adjusted life expectancy (QALE) by income in Korea at the national and district levels. Methods: Mortality rates and EuroQol-5D (EQ-5D) scores were obtained from the National Health Information Database of the National Health Insurance Service and the Korea Community Health Survey, respectively. QALE and differences in QALE among income quintiles were calculated using combined 2008-2014 data for 245 districts in Korea. Correlation analyses were conducted to investigate the associations of neighborhood characteristics with QALE and income gaps therein. Results: QALE showed a graded pattern of inequality according to income, and increased over time for all levels of income and in both sexes, except for low-income quintiles among women, resulting in a widened inequality in QALE among women. In all 245 districts, pro-rich inequalities in QALE were found in both men and women. Districts with higher QALE and smaller income gaps in QALE were concentrated in metropolitan areas, while districts with lower QALE and larger income gaps in QALE were found in rural areas. QALE and differences in QALE by income showed relatively close correlations with socioeconomic characteristics, but relatively weak correlations with health behaviors, except for smoking and indicators related to medical resources. Conclusions: This study provides evidence of income-based inequalities in health measured by QALE in all subnational areas in Korea. Furthermore, QALE and differences in QALE by income were closely associated with neighborhood-level socioeconomic characteristics.

Life Expectancy and Inequalities Therein by Income From 2016 to 2018 Across the 253 Electoral Constituencies of the National Assembly of the Korea

“…The NHID data have been reported to be comparable to data from Statistics Korea for calculating mortality at the national, provincial, and district levels [5]. Using the NHID data, inequalities in life expectancy by income have been explored in prior Korean studies [2,3,6].…”

“…Supplemental Material 1 presents the number of the population and the number of deaths by gender between 2016 and 2018 in each of the electoral constituencies. We used National Health Insurance premiums as a proxy for income, as has been done in previous research [3,[6][7][8][9]. We calculated age group-specific equivalized income, taking household size into account, and categorized equivalized income into quintiles at the nearest quintile points within each electoral constituency.…”

“…Geographical regions are an important factor to consider when comparing the overall health and health equity of a population. In Korea, life expectancy and socioeconomic in- equalities in life expectancy have been explored at the level of administrative districts (i.e., si, gun, and gu, corresponding to cities, counties, and districts, respectively) [1][2][3]. Information on population health and health inequalities in each local administrative district would constitute an important instrument for policy leaders and local government officials to enhance awareness of related problems and to develop and implement policies with the aim of improving health and enhancing health equity.…”

The gaps in health-adjusted life years (HALE) by income and region in Korea: a national representative bigdata analysis

“…South Korea has the lowest birthrate and the fastest aging population rate in the world, and the income gap between regions is also deepening. The gap in QALE by regions is correlated with socioeconomic characteristics, smoking, and health infrastructure related indicators [7]. Therefore, there is a need for further research to identify the origin of regional HALE gap.…”

“…Previous studies have calculated HALE for Korean individuals [6,7]. However, since they used the National Health and Nutrition Examination Survey data to measure the health loss, it has a limitation that only a narrow spectrum of diseases (45 diseases) was included in it.…”